Ariel Katz is the CEO of Sisense with 30 years of experience in technology, cloud products and leading innovation at Microsoft. getty The era of traditional business intelligence (BI)—with its static dashboards and quarterly reports—is coming to an end. As AI evolves from simple automation to sophisticated autonomous agents, companies face a critical inflection point in how they leverage their data.
This shift isn't just about better visualizations or faster reporting; it’s a fundamental transformation in how businesses derive value from their analytics investments. We're seeing this transformation accelerate as AI shifts from general-purpose to vertical-specific applications, exemplified by OpenAI's collaboration with Clay to create a specialized sales agent. These AI agents are increasingly targeting high-volume workflows in customer support, sales operations and supply chain management, analyzing patterns and executing decisions autonomously—provided they can access enterprise data directly without waiting for traditional BI dashboards to update.
BI platforms have relied heavily on human intervention to interpret data, identify insights and execute tasks, introducing inefficiencies and delays. Now, AI agents—which are sophisticated, autonomous entities capable of managing complex workflows, making informed decisions and continuously adapting by learning—can enable businesses to shift from merely accessing tools to directly achieving results. Traditional BI tools were designed for human consumption, generating reports and visualizations that require manual interpretation and action.
AI-driven operations will require a different kind of BI ecosystem. Agentic AI In Action This transformation from pre-scheduled data analysis workflows to real-time analytics is already happening across industries. In manufacturing, agentic AI is monitoring production lines in real time, predicting maintenance needs and automatically adjusting workflows.
In financial services, they're detecting fraud patterns and executing risk mitigation strategies without human intervention. In healthcare, they're optimizing patient scheduling and resource allocation based on real-time demand patterns. One of the key advantages of agentic AI systems is that they learn and optimize over time—users aren’t locked into predefined workflows.
For a retail company planning for holiday sales, modern AI agents can analyze historical data showing that a company has a 30% sales increase during holiday periods. It can combine that historical data with current business forecasts, predicting 5% annual growth, and automatically recommend inventory adjustments so that a company has 35% more inventory going into the holiday season. The AI will recommend which products to stock or limit, and all the human user will need to do is ask the AI to create an appropriate mix for the upcoming season.
An Ecosystem For Agents Achieving success with agentic AI relies on five critical technical foundations: 1. Robust Data Connection Pipelines One of the first steps is to ensure data is flowing in accurately, and monitor data pipelines to ensure that schema or API changes don’t suddenly break the delivery of updated data. Ensure that the company can reliably ingest, prepare, transform and visualize data using familiar cloud data warehouses and Git workflows.
2. Data Warehouse Consolidation Modern AI agents require consistent, reliable access to enterprise data. By consolidating multiple data sources in a data warehouse, organizations create a single source of truth that enables AI agents to work with current information and reduce the risk of conflicting data.
3. Semantic Model Development Once data is centralized, it’s important to make this information accessible to as many different business roles as possible, and this requires a natural language interface that lets people query data using everyday language, known as a semantic layer. A semantic layer is essential to convert raw data into business terms and concepts people understand.
A well-structured semantic layer can also act as a translator between raw enterprise data and AI agents by defining business concepts, relationships, and metrics in a way that AI can interpret and act upon. For example, when an AI agent encounters the term "holiday sales bump," the semantic model helps it understand this means analyzing historical seasonal patterns, current inventory levels and year-over-year growth rates. 4.
Embedded Notebooks To Incorporate Advanced Analytics Workflows Ensure that common, open-source data analysis tools will perform well in a company’s environment to support sophisticated analysis and automatic modeling directly. Capitalize on the flexibility and ubiquity of technologies like SQL, Python and R in embedded Jupyter Notebooks. Embedded notebooks will make it easier to prepare, clean and transform data in one place, to make it easier to integrate findings into self-service analytics.
5. API-First Implementation An API-first architecture serves as the foundation for AI agent integration. Rather than treating APIs as an afterthought, organizations need to design their analytics infrastructure with programmatic access as a core requirement.
This means developing standardized API endpoints for data access, implementing robust authentication mechanisms and ensuring that all analytics capabilities are accessible through documented APIs. The inventory management example above demonstrates how these elements work together: The data warehouse provides accurate historical sales data, the semantic model helps interpret seasonal patterns and business rules and APIs enable the AI agent to query this information and initiate inventory adjustments when needed. The Human Connection The role of humans in this new paradigm is evolving as well.
It doesn't eliminate the need for human oversight—quite the opposite. It elevates human involvement to a more strategic level, where business users focus on setting objectives and fine-tuning AI performance over executing routine tasks. The key is finding the balance between autonomous operation and human guidance, particularly in critical decision-making moments.
Executives will need to think about the implications here as humans focus on managing AI systems, or explore which questions they can find an answer to with the ability to have an AI-based "discussion" with their data. For business leaders, the message is clear: The future of analytics lies not in better dashboards but in creating intelligent systems that can autonomously act on data insights. The transformation from static business intelligence to dynamic, AI-driven analytics isn't just another technology trend—it's a fundamental shift in how businesses operate.
The question isn't whether to make this transition, but how quickly and effectively organizations can adapt to this new reality.
